Predictive coding with spiking neurons and feedforward gist signaling
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Abstract
Abstract Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neural network features such as a non-linear, continuous, and clock-driven function approximator as basic unit of computation. Therefore, we have developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. While adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: 1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and 2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high basal firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature. Author summary How does the brain seamlessly perceive the world, in the midst of chaotic sensory barrage? Rather than passively relaying information that sensory organs pick up from the external world along the cortical hierarchy for a series of feature extractions, it actively gathers statistical regularities from sensory inputs to track causal relationships between physical properties of external objects and the body. In other words, the brain’s perceptual apparatus is constantly trying to make sense of the incoming streams of sensory input and represent the subject’s current situation by building and maintaining internal models of the world and body. While this constructivist theme in understanding perception has been pervasive across multiple disciplines from philosophy to psychology to computer science, a comprehensive theory of brain function called predictive coding aims at unifying neural implementations of perception. In this study, we present a biologically plausible neural network for predictive coding that uses spiking neurons, Hebbian learning, and a feedforward visual pathway to perform perceptual inference and learning on images. Not only does the model show that predictive coding is well behaved under the biological constraint of spiking neurons, but it also provides deep learning and neuromorphic communities with novel paradigms of learning and computational architectures inspired by the nature’s most intelligent system, the brain.
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Following this strategy, researchers have demonstrated, for instance, the neurobiological plausibility of predictive processing-based representational models in spiking neural networks, and reproduced the increase in population sparsity and single-neuron image selectivity observed when ascending the visual cortical hierarchy.
“Following this strategy, researchers have demonstrated, for instance, the neurobiological plausibility of predictive processing-based representational models in spiking neural networks (Lee et al., 2024), and reproduced the increase in population sparsity and singleneuron image selectivity observed when ascending the visual cortical hierarchy (Dora et al., 2021).”
From Integrated information and predictive processing theories of consciousness: An adversarial collaborative review (2026) · cites this paper as Lee et al., 2024
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Flagged first, then by citations
1 of the 101 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Gerstner W. Kistler W. M. Naud R. Paninski L. (2014). Neuronal Dynamics. Cambridge: Cambridge University Press.
- Kant I. (1908). Critique of Pure Reason. 1781. Cambridge, MA: Houghton Mifflin, Modern Classical Philosophers.
Cited by
Sorted by citations
van Holk, M., & Mejías, J. F. (2024) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2024.101351 · Cited by 11 · see citing works
Moreni, G., Dorcioman, R. A., Pennartz, C. M. A., & Mejías, J. F. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012036 · Cited by 10 · see citing works
Self-citationLee, K., Pennartz, C. M. A., & Mejías, J. F. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013469 · Cited by 7 · see citing works
Self-citationFrom quiescence to self-sustained activity: How astrocytes reshape neural dynamics
Garcia, D. W., & Jacquir, S. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.04.009 · Cited by 6 · see citing works
N’dri, A. W., Barbier, T., Teulière, C., & Triesch, J. (2025) · Nature Communications · doi:10.1038/s41467-025-64234-z · Cited by 4 · see citing works
Zhang, M., Chitic, R., & Bohté, S. M. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013112 · Cited by 4 · see citing works
Corcoran, A. W., Haun, A. M., Dorman, R., Tononi, G., Friston, K. J., & Pennartz, C. M. A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106742 · Cited by 2 · see citing works
Self-citationPredictive coding with spiking neural networks: A survey
N’dri, A. W., Gebhardt, W., Teulière, C., Zeldenrust, F., Rao, R. P. N., Triesch, J., & Ororbia, A. G. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108371 · Cited by 5 · see citing works
Imran, B., Riadi, S., Basri, H., Erniwati, S., Zaeniah, Muahidin, Z., Efendi, M. M., & Yani, A. (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11326933 · Cited by 1 · see citing works
Lee, K., Baracco, L., Pennartz, C. M. A., Mototaka Suzuki, A., & Mejías, J. F. (2026) · Current Research in Neurobiology · doi:10.1016/j.crneur.2026.100155 · Cited by 1 · see citing works
Self-citationPredictive processing in neuroscience, computational modeling and psychology
Brucklacher, M., Lee, K., Moreni, G., Mejías, J. F., Bohté, S. M., & Pennartz, C. M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00201-1 · Cited by 1 · see citing works
Self-citationDynamical Analysis of Neuron-Astrocyte Interactions at the Tripartite SynapsePreprint
Garcia, D. W., & Jacquir, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4643799/v1
Kazemi, M. A. A., Esmaeili, H., Radfar, R., & Pilevari, N. (2025) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-025-06790-x
Predictive-Coding-Inspired Reconstruction of Nonlinear Dynamics in Spiking Neural Networks
Batuev, B. B., & Sukhov, S. V. (2026) · Dynamics · doi:10.3390/dynamics6040042
Löffler, H., & Bahmer, A. (2026) · Research Square · doi:10.21203/rs.3.rs-11140391/v1
- Authors
- Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., & Pennartz, C. M. A.
- Affiliations
- University of Amsterdam (NL)
- Loughborough University (GB)
- Corresponding author
- Lee, Kwangjun
- Venue
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2024-04-12
- Volume
- 18
- Pages
- 1338280-1338280
- ISSN
- 1662-5188 (ISSN-L)
- PMC
- PMC11045951
- OpenAlex
- W4394757756
- Cited by
- 14
- References
- 101
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 3
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computational Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Horizon 2020
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- predictive coding, spiking neural networks, Hebbian learning, unsupervised learning, perceptual inference, biologically plausible models, neuromorphic computing, MNIST classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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